These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
156 related articles for article (PubMed ID: 39122887)
1. Based on hematoma and perihematomal tissue NCCT imaging radiomics predicts early clinical outcome of conservatively treated spontaneous cerebral hemorrhage. Song X; Zhang H; Han Y; Lou S; Zhao E; Dong Y; Yang C Sci Rep; 2024 Aug; 14(1):18546. PubMed ID: 39122887 [TBL] [Abstract][Full Text] [Related]
2. Machine Learning-Based Perihematomal Tissue Features to Predict Clinical Outcome after Spontaneous Intracerebral Hemorrhage. Qi X; Hu G; Sun H; Chen Z; Yang C J Stroke Cerebrovasc Dis; 2022 Jun; 31(6):106475. PubMed ID: 35417846 [TBL] [Abstract][Full Text] [Related]
3. Perihematomal edema-based CT-radiomics model to predict functional outcome in patients with intracerebral hemorrhage. Huang X; Wang D; Ma Y; Zhang Q; Ren J; Zhao H; Li S; Deng J; Yang J; Zhao Z; Xu M; Zhou Q; Zhou J Diagn Interv Imaging; 2023 Sep; 104(9):391-400. PubMed ID: 37179244 [TBL] [Abstract][Full Text] [Related]
4. Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model. Xie H; Ma S; Wang X; Zhang X Eur Radiol; 2020 Jan; 30(1):87-98. PubMed ID: 31385050 [TBL] [Abstract][Full Text] [Related]
5. Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison. Yang WS; Liu JY; Shen YQ; Xie XF; Zhang SQ; Liu FY; Yu JL; Ma YB; Xiao ZS; Duan HW; Li Q; Chen SX; Xie P J Stroke Cerebrovasc Dis; 2024 Jul; 33(7):107731. PubMed ID: 38657831 [TBL] [Abstract][Full Text] [Related]
6. Radiomics for prediction of intracerebral hemorrhage outcomes: A retrospective multicenter study. Huang X; Wang D; Zhang Q; Ma Y; Zhao H; Li S; Deng J; Ren J; Yang J; Zhao Z; Xu M; Zhou Q; Zhou J Neuroimage Clin; 2022; 36():103242. PubMed ID: 36279754 [TBL] [Abstract][Full Text] [Related]
7. Noncontrast Computed Tomography-Based Radiomics Analysis in Discriminating Early Hematoma Expansion after Spontaneous Intracerebral Hemorrhage. Song Z; Guo D; Tang Z; Liu H; Li X; Luo S; Yao X; Song W; Song J; Zhou Z Korean J Radiol; 2021 Mar; 22(3):415-424. PubMed ID: 33169546 [TBL] [Abstract][Full Text] [Related]
8. Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography. Feng C; Ding Z; Lao Q; Zhen T; Ruan M; Han J; He L; Shen Q Eur Radiol; 2024 May; 34(5):2908-2920. PubMed ID: 37938384 [TBL] [Abstract][Full Text] [Related]
9. Hybrid clinical-radiomics model based on fully automatic segmentation for predicting the early expansion of spontaneous intracerebral hemorrhage: A multi-center study. Wang M; Liang Y; Li H; Chen J; Fu H; Wang X; Xie Y J Stroke Cerebrovasc Dis; 2024 Nov; 33(11):107979. PubMed ID: 39222703 [TBL] [Abstract][Full Text] [Related]
10. A Radiomics Model Based on CT Images Combined with Multiple Machine Learning Models to Predict the Prognosis of Spontaneous Intracerebral Hemorrhage. Pei L; Fang T; Xu L; Ni C World Neurosurg; 2024 Jan; 181():e856-e866. PubMed ID: 37931880 [TBL] [Abstract][Full Text] [Related]
11. Combination of Hematoma Volume and Perihematoma Radiomics Analysis on Baseline CT Scan Predicts the Growth of Perihematomal Edema. Wang J; Xiong X; Zou J; Fu J; Yin Y; Ye J Clin Neuroradiol; 2023 Mar; 33(1):199-209. PubMed ID: 35943522 [TBL] [Abstract][Full Text] [Related]
12. Clinical Features, Non-Contrast CT Radiomic and Radiological Signs in Models for the Prediction of Hematoma Expansion in Intracerebral Hemorrhage. Chen ZF; Zhang L; Carrington AM; Thornhill R; Miguel O; Auriat AM; Omid-Fard N; Hiremath S; Tshemeister Abitbul V; Dowlatshahi D; Demchuk A; Gladstone D; Morotti A; Casetta I; Fainardi E; Huynh T; Elkabouli M; Talbot Z; Melkus G; Aviv RI Can Assoc Radiol J; 2023 Nov; 74(4):713-722. PubMed ID: 37070854 [TBL] [Abstract][Full Text] [Related]
13. A clinical-radiomics nomogram may provide a personalized 90-day functional outcome assessment for spontaneous intracerebral hemorrhage. Song Z; Tang Z; Liu H; Guo D; Cai J; Zhou Z Eur Radiol; 2021 Jul; 31(7):4949-4959. PubMed ID: 33733691 [TBL] [Abstract][Full Text] [Related]
15. An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study. Zhang H; Yang YF; Song XL; Hu HJ; Yang YY; Zhu X; Yang C BMC Med Imaging; 2024 Jul; 24(1):170. PubMed ID: 38982357 [TBL] [Abstract][Full Text] [Related]
16. Development and validation of a clinical-radiomics nomogram for predicting a poor outcome and 30-day mortality after a spontaneous intracerebral hemorrhage. Xie Y; Chen F; Li H; Wu Y; Fu H; Zhong Q; Chen J; Wang X Quant Imaging Med Surg; 2022 Oct; 12(10):4900-4913. PubMed ID: 36185057 [TBL] [Abstract][Full Text] [Related]
17. Perihematomal Diffusion Restriction in Intracerebral Hemorrhage Depends on Hematoma Volume, But Does Not Predict Outcome. Stösser S; Neugebauer H; Althaus K; Ludolph AC; Kassubek J; Schocke M Cerebrovasc Dis; 2016; 42(3-4):280-7. PubMed ID: 27222302 [TBL] [Abstract][Full Text] [Related]
18. Non-Contrast CT-Based Radiomics Score for Predicting Hematoma Enlargement in Spontaneous Intracerebral Hemorrhage. Li H; Xie Y; Liu H; Wang X Clin Neuroradiol; 2022 Jun; 32(2):517-528. PubMed ID: 34324004 [TBL] [Abstract][Full Text] [Related]
19. Radiomics features on non-contrast computed tomography predict early enlargement of spontaneous intracerebral hemorrhage. Li H; Xie Y; Wang X; Chen F; Sun J; Jiang X Clin Neurol Neurosurg; 2019 Oct; 185():105491. PubMed ID: 31470362 [TBL] [Abstract][Full Text] [Related]
20. Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approach. Lu M; Wang Y; Tian J; Feng H Diagn Interv Radiol; 2024 Sep; 30(5):299-312. PubMed ID: 38654561 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]